key: cord-320860-qt84oicg
authors: Zhang, Aining; Leng, Yan; Zhang, Yi; Wu, Kefan; Ji, Yelong; Lei, Shaoqing; Xia, Zhongyuan
title: Meta-Analysis of coagulation parameters associated with disease severity and poor prognosis of COVID-19
date: 2020-09-15
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2020.09.021
sha: 
doc_id: 320860
cord_uid: qt84oicg

Background To figure out whether abnormal coagulation parameters are associated with disease severity and poor prognosis in patients with 2019 Corona Virus Disease (COVID-19). Methods A systematic literature search was conducted using the databases PubMed, Embase, and Web of sciences until April 25, 2020. We included a total of 15 studies with 2277 patients. Platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D) and fibrinogen (FIB) were collected and analyzed. The statistical results were expressed the effect measure by mean difference (MD) with the related 95% confidence interval (CI). Results The PLT level of severe patients was lower than that of mild patients, while the levels of PT, D-D and FIB were higher than those of mild patients (P < 0.05). The level of APTT had no statistical difference between two groups (P > 0.05). Compared to Non-ICU patients, PT of ICU patients was significantly longer (P < 0.05). In Non-survivors, PT and D-D were higher, yet PLT was lower than survivors (P < 0.05). There was no significant difference in APTT between survivors and Non-survivors (P > 0.05). The funnel plot and Egger's Regression test demonstrated that there was no publication bias. Conclusions Our data support the notion that coagulopathy could be considered as a risk factor for disease severity and mortality of COVID-19, which may help clinicians to identify the incidence of poor outcomes in COVID-19 patients.

In early December 2019, a new coronavirus named severe acute respiratory syndrome coronavirus 2(SARS-COV-2) caused a catastrophic international phenomenon of the respiratory disease COVID-19 1 . This is the third serious coronavirus outbreak in less than 20 years, following SARS in 2002 and MERS in 2012 2 . Since the outbreak, this innovative type of pneumonia, far more contagious than SARS, has spread rapidly around the world, posing a serious threat to human life and health 3 . Confirmed cases have been reported in 216 countries, areas or territories. As of 23 July 2020, a total of 15,012,731 cases, including 619,150 deaths, have been reported worldwide 4 . Although about 15% of cases caused by human coronavirus strains are the common cold, SARS-COV-2 infection can have a variety of manifestations ranging in severity from influenza to death 2 . Therefore, the identification of certain laboratory parameters that could distinguish between severe and non-severe cases, or between high and low risk of death, will help to improve the understanding of the clinical situation 5 .

The most common manifestations of COVID-19 infection are fever, cough, and progressive dyspnea caused by respiratory infection 6 . Emerging evidence suggested that severe COVID-19 may be complicated with coagulopathy, and even severe cases may cause disseminated intravascular coagulation (DIC) 7 . Research report by Tang 8 et al. showed that 71.4% of patients who died of coronavirus met ISTH criteria for DIC.

However, a recent study suggested that the characteristics of COVID-19-associated J o u r n a l P r e -p r o o f coagulopathy(CAC) are different from clotting disorders caused by bacterial infections and other diseases. CAC usually presents with elevated D-dimer and fibrinogen levels, but there are few abnormalities in the prothrombin time and platelet count during the initial course of the disease 9 . In order to explore the relationship between coagulopathy and the severity and prognosis of the disease, we conducted this meta-analysis to compare the difference in blood coagulation parameters among COVID-19 patients.

We conducted a systematic review using PubMed, Embase, and Web of Science databases with the keywords "laboratory" in all fields AND "COVID-19" OR "2019 novel coronavirus disease" OR "COVID-19 pandemic" OR "2019 novel coronavirus infection" OR "2019-nCoV infection" OR "2019-nCoV disease" OR "COVID-19 virus infection" OR "wuhan coronavirus", between 2019 and present time (i.e., April 25, 2020) , with no language restrictions. The reference lists of selected studies were also checked for identifying additional eligible studies. All included studies were managed by EndNoteX9.2 software and duplicates were removed.

Our inclusion criteria included (1) study population: adult patients (>18 years of age) who were laboratory-confirmed or clinically diagnosed as COVID-19; (2) study design:

cross-sectional study, prospective/retrospective cohort study, case-control study, and randomized controlled trials. Our exclusion criteria included (1) asymptomatic patients; J o u r n a l P r e -p r o o f (2) studies without reporting coagulation parameters; (3) systematic reviews, metaanalyses, editorials and other forms not presenting original data. The results of the initial search strategy were first screened by title and abstract to exclude apparently irrelevant articles. Remainings were delivered the full text to further screen based on inclusion and exclusion criteria. Two reviewers independently examined the literature, and when there was any disagreement, the opinion of a third researcher was sought to resolve it through discussion.

Two reviewers independently extracted the following data from the included references: patient basic characteristics (age and sex), clinical classification or clinical outcome and coagulation parameters. There were five coagulation parameters included: PLT, PT, APTT, D-D and FIB. A third researcher checked the data extraction to ensure compliance with our inclusion criteria and the accuracy of the data. Continuous variables were presented as mean ± standard deviation (SD). If variables were represented by median and interquartile range (IQR), we used Excel software to convert them to the form of mean ±SD. The data was meta-analyzed using Revman5.3 software provided by the Cochrane collaboration. The statistical results were expressed the effect measure by mean difference (MD) with the related 95% confidence interval (CI).

Heterogeneity analysis of the included studies was carried out by I 2, , an indicator in percentages used to determine whether the fixed effect model or random effect model was applied. "I 2 > 50%" considered the heterogeneity to be statistically significant that the random effect model was adopted for analysis; otherwise, the fixed effect model J o u r n a l P r e -p r o o f was selected. The level of meta-analysis was equal to 0.05.

The AXIS tool 10 was used to score the methodological quality of included studies, which is a critical appraisal tool to assess study design, reporting quality and the risk of bias in cross-sectional studies 11 . The components of the AXIS tool consist 20 questions, each of which could be answered "yes" (1 point) or "no or don't know/comment" (0 point). A funnel plot was developed using Stata12.0 software to assess publication bias.

Meanwhile, Egger's regression test was applied to make a quantitative analysis of publication bias.

The initial search identified 1209 potentially relevant citations through PubMed database and 43 through other sources (Fig. 1) . After eliminating the duplicated literature as well as reading titles and abstracts, 39 articles were screened out for fulltext assessment. Of these, 28 were excluded for reasons listed in Fig. 1 . Four additional studies were identified by reading the reference lists of the selected documents, thus, the pooled analysis finally included 15 studies 8, 12-25 . We listed the basic characteristics and quality score of each study included in Table 1 . All the studies were cross-sectional studies conducted in China, involving a total of 2277 patients with sample sizes ranging from 30 to 449. Among them, 7 studies were included to evaluate differences in coagulation function between mild and severe patients, 4 between ICU and Non-ICU J o u r n a l P r e -p r o o f patients and 5 between survivors and Non-survivors. All the statistical results were presented in Table 2 , as well as visually displayed through the forest plots. (Fig. 2 ). (Fig. 3) .

We evaluated four indicators of PLT, PT, APTT and D-D to investigate coagulation function between survivors and Non-survivors, included 3, 5, 2, 5 studies respectively.

Referred to the I 2 value, We used the fixed effect model to compare the differences of PLT and PT between the two groups (I 2 =0, 38%, separately) and the random effect model to compare APTT and D-D between the two groups (I=70%, 81%, separately).

The statistics showed that PLT of survivors was higher than that of Non-survivors (Fig. 4 ).

Studies comparing D-D indicator were used to draw a funnel plot (Fig. 5) for the analysis of publication bias. The selected researches were distributed in the plot in a basically symmetrical way, indicating that the possible bias was small. For further quantitative analysis, we conducted Egger's regression test (P=0.923) and confirmed that there was no significant statistically evidence of publication bias (Table 3) .

Although the mortality rate of this novel coronary pneumonia is lower than that of SARS and MERS, the risk of severe and critically ill patients progressing to ARDS and being admitted to ICU still remains fairly high 26 . There is an urgent need to identify a few indicators for early diagnosis of disease progression and prognosis in order to provide more appropriate treatment options. Studies have shown that the cytokines IL-6 and procalcitonin can be used to predict the severity of COVID-19 9 33 . The current view is that SARS-COV-2 enters host cells through cell surface receptor, ACE2. This process leads to local inflammation, endothelial activation, tissue damage, and cytokine release changes that lead to coagulation activation 34, 35 .

Another perspective is that the virus interferes, directly or indirectly, with the clotting pathways. The susceptibility of these two pathways to coagulation disorders is mainly related to host factors such as age, comorbidities, and degree of lung injury 36 In conclusion, this study demonstrated beneficial of screening abnormal coagulation parameters, such as decreased PLT, elevated PT, D-D and FIB for predicting the severity and prognosis of COVID-19. We suggest clinicians to pay attention to changes in blood coagulation of COVID-19 patients and explore their potential guidance for therapy.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The study does not require ethical approval because the meta-analysis are based on published research and the original data are anonymous.

The authors declared that they have no conflicts of interest to this work.

We declare that there are no potential conflicts of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Declaration of i Declaration of i Declaration of i Declaration of interest nterest nterest nterests s s s The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. -, Not available, not reported. 

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